Deep Multi-modal Species Occupancy Modeling

Abstract

Effective conservation and restoration of species is an increasingly urgent priority. To design management strategies that improve species success, we need a solid understanding of the habitat characteristics that support it. Occupancy models are statistical tools that ecologists use to model these relationships from data. Yet, current models represent habitats with coarse-scale environmental variables that fail to capture important microhabitat features. We show that these limitations can be addressed by incorporating AI-derived, multimodal habitat representations from overhead satellite imagery and ground-level camera-trap imagery. Across geography and species, these representations yield more accurate out-of-sample predictions than models based on conventional covariates alone, and combining satellite and ground-level views provides complementary gains. To translate improved prediction into actionable ecological insight, we further introduce a method that makes black-box AI-derived habitat representations interpretable by summarizing key factors contributing to occupancy probability into text-based descriptions. We then generate a per-site score for each description, which can replace black-box features to transparently link discovered habitat elements to species occurrence while maintaining predictive performance. Our approach provides a path toward microhabitat-aware and interpretable species-habitat models that support restoration planning and management decisions. We implement our method in an open-source Python package bridging AI and statistical ecology.